The present invention relates to estimating electrophysiological maps of a patient, and more particularly to estimating electrophysiological maps of a patient from medical image data and electrocardiography data of the patient.
Cardiac disease is the number one cause of death in the United States. More than two million Americans have been diagnosed with irregular heartbeats, which are clinically referred to as cardiac arrhythmias. On the other hand, more than five million Americans have been diagnosed with some level of deficiency to properly pump blood, which is clinically referred to as congestive heart failure. Treatment of arrhythmias is primarily based on drug therapy. However, when arrhythmias become pharmacologically resistant, radio-frequency ablation therapy becomes the treatment of choice. Deficiency in pump function due to asynchronized ventricular contraction is often corrected using cardiac resynchronization therapy (CRT). While these treatments are providing more and more effective in improving patients' lifestyle and life span, the clinical outcomes are often still not satisfactory. Cardiac ablation success rates, specifically in the longer term, are low. In fact, more than 40% of patients treated require that the cardiac ablation procedure be repeated. CRT treatment has a success rate of 2/3. One out of three patients does not respond properly to the CRT treatment. For these patients, it is suspected that patient selection, along with the optimization of the lead location and device settings in the CRT treatment, are important factors as to why the CRT treatment is not successful.
Electrocardiography (ECG) is the main method for assessing a patient's cardiac electrophysiology. However, for accurate diagnosis of the cause of an arrhythmia and planning of a cardiac ablation or CRT procedure, more information is need than the electrocardiography information provided by a 12-lead ECG only. In particular, it is of high importance to know the myocardium region that causes the arrhythmia or that maximizes resynchronization. To that end, current approaches use endocardial potential and activation time maps generated using catheter based measurements. However, a limitation of such approaches is that they are invasive and also acquired over several heart beats. As a result, identifying and analyzing the complex structure of the electrical wave during arrhythmias (e.g., atrial fibrillation, ventricular tachycardia) is impossible.